List of AWS Auto Scaling Customers
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Since 2010, our global team of researchers has been studying AWS Auto Scaling customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased AWS Auto Scaling for Application Hosting and Computing Services from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using AWS Auto Scaling for Application Hosting and Computing Services include: Netflix, a United States based Media organisation with 14000 employees and revenues of $39.00 billion, Lyft, a United States based Professional Services organisation with 2934 employees and revenues of $5.79 billion, Hudl, a United States based Media organisation with 2200 employees and revenues of $320.0 million and many others.
Contact us if you need a completed and verified list of companies using AWS Auto Scaling, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The AWS Auto Scaling customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Hudl | Media | 2200 | $320M | United States | Amazon Web Services (AWS) | AWS Auto Scaling | Application Hosting and Computing Services | 2014 | n/a |
In 2014 Hudl deployed AWS Auto Scaling within its Application Hosting and Computing Services footprint to scale its video ingestion, encoding and analytics platform. The implementation was focused on automated cluster scaling to match compute capacity with episodic demand for media processing workloads.
AWS Auto Scaling was configured to drive cluster elasticity for video ingestion, encoding and analytics, allowing clusters to automatically scale up to meet peak processing needs and scale down when demand subsides. Hudl documented operational nights where the platform spun up approximately 2,000 servers for encoding, and the configuration prioritized rapid provisioning of compute capacity for encoding pipelines and ingestion queues.
This US implementation extended across Hudl's media processing operations and impacted engineering, operations and media workflows responsible for uploads and encoding. Hudl reported improved upload and encoding throughput and reduced costs through automatic scale down, aligning Application Hosting and Computing Services controls with operational demand for its video platform.
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Lyft | Professional Services | 2934 | $5.8B | United States | Amazon Web Services (AWS) | AWS Auto Scaling | Application Hosting and Computing Services | 2014 | n/a |
In 2014, Lyft implemented AWS Auto Scaling within its Application Hosting and Computing Services to manage capacity for its US based ride hailing platform. Lyft configured AWS Auto Scaling to automatically expand and contract an EC2 backed microservices fleet, aligning compute capacity with real time demand spikes.
The implementation used core AWS Auto Scaling capabilities such as autoscaling groups, scaling policies and lifecycle management to add and remove EC2 instances in response to load, and to automate resource provisioning across service tiers. Configuration focused on service level capacity for ride matching, dispatch, and API gateway workflows, ensuring compute was provisioned at the microservice level rather than at monolithic instance granularity.
Operational integration was directly with EC2 and the AWS control plane for monitoring and scale event triggers, and the implementation operated across Lyft's US production environment during major launches and peak usage windows. Governance moved capacity decisions into automated scaling rules and policies, reducing the need for manual provisioning and enabling predictable platform behavior during surges.
The deployment of AWS Auto Scaling allowed Lyft to handle up to eight times more riders during peak periods, improved availability during major launches, and reduced infrastructure costs by scaling down automatically after peaks.
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Netflix | Media | 14000 | $39.0B | United States | Amazon Web Services (AWS) | AWS Auto Scaling | Application Hosting and Computing Services | 2018 | n/a |
In 2018, Netflix integrated AWS Auto Scaling with its Titus container management platform to drive automated capacity adjustments for streaming services and backend microservices, aligning the work within the Application Hosting and Computing Services category. The engagement used Amazon Web Services tooling, specifically AWS Auto Scaling and Application Auto Scaling patterns documented by Netflix engineering, to operationalize scalable capacity for production services.
Implementation centered on embedding AWS Auto Scaling controls into Titus orchestration, enabling policy-driven capacity management for both control plane components and customer-facing streaming services. Functional capabilities implemented included metrics-driven scaling policies and application auto scaling patterns that allowed Titus to request capacity changes automatically based on observed load and service signals.
Integration scope included tight coupling between Titus and AWS Auto Scaling APIs across Netflixs US and global footprint, covering streaming service instances and a spectrum of backend microservices. The work explicitly treated control-plane scaling and service-level scaling as coordinated concerns, ensuring predictable, fast scaling behavior during demand surges and routine operational changes.
Governance and rollout were managed through engineering documentation and platform-level configuration inside Titus, with scaling policies and operational runbooks codified to ensure repeatable behavior across services. The collaboration helped enable predictable, fast scaling of control-plane and streaming services, and it contributed to AWS making the Application Auto Scaling feature generally available, as described in Netflixs engineering post.
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